Endpoint Grouping and Targeted Package Deployment
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Solution Overview
Problem
Managing software updates for complex systems like modern vehicles and IoT devices is challenging due to the unknown properties and capabilities of the devices, leading to difficulties in targeting and deploying upgrades effectively.
Innovation Solution
A data model and management system that uses propertyKey-propertyValue pairs to represent dynamic properties of components, groups endpoints based on shared characteristics, and deploys upgrades through a query grammar-based filtering system, ensuring that only relevant packages are delivered to endpoints that meet specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software updates are deployed to all endpoints without filtering, then update coverage is maximized, but unnecessary updates are delivered increasing complexity and resource usage
Solution Approach 1:
The patent segments endpoints into distinct groups based on shared properties and characteristics. Each group can be targeted with specific software updates, allowing the system to deliver updates only to relevant endpoints rather than all endpoints universally. This segmentation enables efficient update deployment by matching updates to appropriate endpoint groups, improving productivity while avoiding the complexity of managing universal updates across diverse endpoints.
Solution Approach 2:
The patent applies local quality by tailoring software updates to specific endpoint groups based on their unique properties. Instead of applying a uniform update strategy to all endpoints, the system customizes update delivery according to the specific characteristics of each endpoint group. This ensures that each endpoint receives only the updates relevant to its configuration, reducing unnecessary complexity while maintaining high deployment efficiency.
2Measurement precision
If comprehensive device properties are collected and stored, then targeting precision is improved, but data storage requirements and system complexity increase
Solution Approach 1:
The patent merges endpoints that share common properties into groups, consolidating the storage requirements. Instead of storing and processing individual properties for each endpoint separately, the system stores group-level property definitions that represent multiple endpoints. This merging approach maintains high targeting precision by preserving the necessary property information while significantly reducing the total quantity of data that must be stored and managed.
Solution Approach 2:
The patent creates universal group definitions that can represent multiple endpoints with similar characteristics. Each group definition serves as a template that can be applied to numerous endpoints, allowing the system to achieve precise targeting without storing redundant property data for each individual endpoint. This universality principle enables the system to maintain high measurement precision while minimizing data storage requirements through reusable group templates.
Data Source
AI summary
Data structures are stored in an endpoint data store. Each data structure represents an endpoint having one or more components, each component having one or more arbitrary properties. Multiple groups of endpoints are identified by name and defined by group criteria expressed in terms of the one or more arbitrary properties of the one or more components of the endpoints that are members of the group. For each group, the group criteria of the group is stored in a data store in association with the name of the group. Packages and releases may be targeted to endpoints in various manners.